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neural-networks

3 posts

google

NeuralGCM harnesses AI to better simulate long-range global precipitation (opens in new tab)

NeuralGCM represents a significant evolution in atmospheric modeling by combining traditional fluid dynamics with neural networks to solve the long-standing challenge of simulating global precipitation. By training the AI component directly on high-quality NASA satellite observations rather than biased reanalysis data, the model achieves unprecedented accuracy in predicting daily weather cycles and extreme rainfall events. This hybrid approach offers a faster, more precise tool for both medium-range weather forecasting and multi-decadal climate projections. ## The Limitations of Cloud Parameterization * Precipitation is driven by cloud processes occurring at scales as small as 100 meters, which is far below the kilometer-scale resolution of global weather models. * Traditional models rely on "parameterizations," or mathematical approximations, to estimate how these small-scale events affect the larger atmosphere. * Because these approximations are often simplified, traditional models struggle to accurately capture the complexity of water droplet formation and ice crystal growth, leading to errors in long-term forecasts. ## Training on Direct Satellite Observations * Unlike previous AI models trained on "reanalyses"—which are essentially simulations used to fill observational gaps—NeuralGCM is trained on NASA satellite-based precipitation data spanning 2001 to 2018. * The model utilizes a differentiable dynamical core, an architecture that allows the neural network to learn the effects of small-scale events directly from physical observations. * By bypassing the weaknesses inherent in reanalysis data, the model effectively creates a machine-learned parameterization that is more faithful to real-world cloud physics. ## Performance in Weather and Climate Benchmarks * At a resolution of 280 km, NeuralGCM outperforms leading operational models in medium-range forecasts (up to 15 days) and matches the precision of sophisticated multi-decadal climate models. * The model shows a marked improvement in capturing precipitation extremes, particularly for the top 0.1% of rainfall events. * Evaluation through WeatherBench 2 demonstrates that NeuralGCM accurately reproduces the diurnal (daily) weather cycle, a metric where traditional physics-based models frequently fall short. NeuralGCM provides a highly efficient and accessible framework for researchers and city planners who need to simulate long-range climate scenarios, such as 100-year storms or seasonal agricultural cycles. Its ability to maintain physical consistency while leveraging the speed of AI makes it a powerful candidate for the next generation of global atmospheric modeling.

neural-networksaimachine-learningclimate-modeling+5
google

Reducing EV range anxiety: How a simple AI model predicts port availability (opens in new tab)

Google Research has developed a lightweight AI model designed to predict the probability of EV charging port availability at specific future intervals, directly addressing the "range anxiety" experienced by electric vehicle drivers. By co-designing the model with deployment infrastructure, researchers found that a simple linear regression approach outperformed more complex architectures like neural networks and decision trees. The resulting system effectively predicts availability changes during high-turnover periods, providing more reliable navigation and planning data than traditional "no-change" assumptions. ### Model Architecture and Feature Selection * The development team prioritized a minimal feature set to ensure low-latency deployment and high speed in real-world navigational applications. * After testing various architectures, a straightforward linear regression model was selected for its robustness and superior performance in this specific predictive task. * The model was trained using real-time availability data from diverse geographical regions, specifically California and Germany, with an emphasis on larger charging stations that reflect high-traffic usage patterns. ### Temporal Feature Weights and Occupancy Trends * The model uses the hour of the day as a primary feature, treating each hour as an independent variable to capture specific daily cycles. * Learned numerical "weights" dictate the predicted rate of occupancy change: positive weights indicate ports are becoming occupied (e.g., during morning rush), while negative weights indicate ports are being freed up (e.g., during evening hours). * The system is designed to only deviate from the current occupancy state when the change rate is statistically significant or when a station's large size amplifies the likelihood of a status change. ### Performance Benchmarking and Validation * The model was evaluated against a "Keep Current State" baseline, which assumes future availability will be identical to the present status—a difficult baseline to beat since port status remains unchanged roughly 90% of the time over 30-minute windows. * Accuracy was measured using Mean Squared Error (MSE) and Mean Absolute Error (MAE) over 30-minute and 60-minute time horizons across 100 randomly selected stations. * Testing confirmed that the linear regression model provides its greatest value during infrequent but critical moments of high turnover, successfully identifying when a station is likely to become full or available. The success of this model demonstrates that sophisticated deep learning is not always the optimal solution for infrastructure challenges. By combining intuitive real-world logic—such as driver schedules and station capacity—with simple machine learning techniques, developers can create highly efficient tools that significantly improve the EV user experience without requiring massive computational overhead.

neural-networksaimachine-learningpredictive-modeling+4
google

From research to climate resilience (opens in new tab)

Google Research is leveraging advanced artificial intelligence to transform climate science from theoretical exploration into scalable, real-world resilience tools. By developing sophisticated models for floods, cyclones, and hyper-local weather, the initiative provides critical lead times that empower communities to protect lives and livelihoods against increasingly frequent environmental threats. This transition from "impossible" research to global implementation highlights AI's capacity to bridge data gaps in the world's most vulnerable regions. ## AI-Powered Global Flood Forecasting * Google developed a global hydrological AI model, recently published in *Nature*, which enables riverine flood forecasts up to seven days in advance. * The system utilizes "virtual gauges" to analyze historical data and provide predictions in regions where physical water-monitoring infrastructure is non-existent. * The Flood Hub platform now covers over 100 countries and 700 million people, providing an expert data layer and API access for local governments and researchers. ## Cyclone Tracking and Intensity Prediction * Collaborative research between Google DeepMind and Google Research has produced models that predict storm existence, track, intensity, and size up to 15 days in advance. * The AI generates up to 50 different possible scenarios for each storm, providing a more nuanced view of potential impact than traditional physics-based supercomputer simulations. * Through the new Weather Lab website, these experimental models are being shared with the US National Hurricane Center to assist in forecasting during the Atlantic hurricane season. ## Global Nowcasting with MetNet-3 * The MetNet-3 state-of-the-art neural weather model provides hyper-local precipitation forecasts with a 5km resolution, updated every 15 minutes. * By utilizing satellite observations instead of traditional ground-based radar, the system delivers reliable weather data to regions like Africa that lack extensive physical infrastructure. * These 12-hour "nowcasting" windows are integrated directly into Google Search, specifically helping agricultural communities react to changing conditions to improve crop yields and reduce waste. These advancements demonstrate that the "art of the possible" is rapidly expanding, offering a future where data-scarce regions can access the same life-saving predictive capabilities as developed nations through global partnerships and satellite-based modeling.

neural-networksaimachine-learningweather-forecasting+4